Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations60398
Missing cells15
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory72.0 B

Variable types

Text2
Numeric7

Alerts

sls_due_dt is highly overall correlated with sls_order_dt and 1 other fieldsHigh correlation
sls_order_dt is highly overall correlated with sls_due_dt and 1 other fieldsHigh correlation
sls_price is highly overall correlated with sls_salesHigh correlation
sls_sales is highly overall correlated with sls_priceHigh correlation
sls_ship_dt is highly overall correlated with sls_due_dt and 1 other fieldsHigh correlation
sls_order_dt is highly skewed (γ1 = -56.34108454)Skewed
sls_quantity is highly skewed (γ1 = 162.1480759)Skewed

Reproduction

Analysis started2025-11-05 12:43:59.756930
Analysis finished2025-11-05 12:44:04.754430
Duration5 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct27659
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
2025-11-05T13:44:04.902175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters422786
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9668 ?
Unique (%)16.0%

Sample

1st rowSO43697
2nd rowSO43698
3rd rowSO43699
4th rowSO43700
5th rowSO43701
ValueCountFrequency (%)
so588458
 
< 0.1%
so726568
 
< 0.1%
so707148
 
< 0.1%
so515557
 
< 0.1%
so602337
 
< 0.1%
so719617
 
< 0.1%
so748697
 
< 0.1%
so645427
 
< 0.1%
so629847
 
< 0.1%
so585727
 
< 0.1%
Other values (27649)60325
99.9%
2025-11-05T13:44:05.162583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S60398
14.3%
O60398
14.3%
647143
11.2%
544004
10.4%
735344
8.4%
430836
7.3%
225066
5.9%
324928
5.9%
123946
 
5.7%
023824
 
5.6%
Other values (2)46899
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301990
71.4%
Uppercase Letter120796
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
647143
15.6%
544004
14.6%
735344
11.7%
430836
10.2%
225066
8.3%
324928
8.3%
123946
7.9%
023824
7.9%
823580
7.8%
923319
7.7%
Uppercase Letter
ValueCountFrequency (%)
S60398
50.0%
O60398
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common301990
71.4%
Latin120796
 
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
647143
15.6%
544004
14.6%
735344
11.7%
430836
10.2%
225066
8.3%
324928
8.3%
123946
7.9%
023824
7.9%
823580
7.8%
923319
7.7%
Latin
ValueCountFrequency (%)
S60398
50.0%
O60398
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII422786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S60398
14.3%
O60398
14.3%
647143
11.2%
544004
10.4%
735344
8.4%
430836
7.3%
225066
5.9%
324928
5.9%
123946
 
5.7%
023824
 
5.6%
Other values (2)46899
11.1%
Distinct130
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
2025-11-05T13:44:05.302024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length7
Mean length8.1647074
Min length7

Characters and Unicode

Total characters493132
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBK-R93R-62
2nd rowBK-M82S-44
3rd rowBK-M82S-44
4th rowBK-R50B-62
5th rowBK-M82S-44
ValueCountFrequency (%)
wb-h0984244
 
7.0%
pk-70983191
 
5.3%
tt-m9283095
 
5.1%
tt-r9822376
 
3.9%
hl-u509-r2230
 
3.7%
ca-10982190
 
3.6%
hl-u509-b2125
 
3.5%
fe-66542121
 
3.5%
hl-u5092085
 
3.5%
bc-m0052025
 
3.4%
Other values (120)34716
57.5%
2025-11-05T13:44:05.526598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
-87602
17.8%
938742
 
7.9%
038558
 
7.8%
832143
 
6.5%
B30620
 
6.2%
T25939
 
5.3%
224470
 
5.0%
R20829
 
4.2%
K18396
 
3.7%
417737
 
3.6%
Other values (23)158096
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number210589
42.7%
Uppercase Letter194941
39.5%
Dash Punctuation87602
17.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B30620
15.7%
T25939
13.3%
R20829
10.7%
K18396
9.4%
M15706
8.1%
H14194
7.3%
L12611
 
6.5%
S7918
 
4.1%
U7723
 
4.0%
C7397
 
3.8%
Other values (12)33608
17.2%
Decimal Number
ValueCountFrequency (%)
938742
18.4%
038558
18.3%
832143
15.3%
224470
11.6%
417737
8.4%
517011
8.1%
615299
 
7.3%
110973
 
5.2%
77905
 
3.8%
37751
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
-87602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common298191
60.5%
Latin194941
39.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
B30620
15.7%
T25939
13.3%
R20829
10.7%
K18396
9.4%
M15706
8.1%
H14194
7.3%
L12611
 
6.5%
S7918
 
4.1%
U7723
 
4.0%
C7397
 
3.8%
Other values (12)33608
17.2%
Common
ValueCountFrequency (%)
-87602
29.4%
938742
13.0%
038558
12.9%
832143
 
10.8%
224470
 
8.2%
417737
 
5.9%
517011
 
5.7%
615299
 
5.1%
110973
 
3.7%
77905
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII493132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-87602
17.8%
938742
 
7.9%
038558
 
7.8%
832143
 
6.5%
B30620
 
6.2%
T25939
 
5.3%
224470
 
5.0%
R20829
 
4.2%
K18396
 
3.7%
417737
 
3.6%
Other values (23)158096
32.1%

sls_cust_id
Real number (ℝ)

Distinct18484
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18841.685
Minimum11000
Maximum29483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2025-11-05T13:44:05.604170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11000
5-th percentile11414
Q114003
median18143
Q323429.75
95-th percentile28108.15
Maximum29483
Range18483
Interquartile range (IQR)9426.75

Descriptive statistics

Standard deviation5432.4304
Coefficient of variation (CV)0.28831977
Kurtosis-1.1617483
Mean18841.685
Median Absolute Deviation (MAD)4594.5
Skewness0.28365312
Sum1.1380001 × 109
Variance29511300
MonotonicityNot monotonic
2025-11-05T13:44:05.696620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1118568
 
0.1%
1130067
 
0.1%
1127765
 
0.1%
1126263
 
0.1%
1128762
 
0.1%
1117660
 
0.1%
1109159
 
0.1%
1133158
 
0.1%
1156658
 
0.1%
1133057
 
0.1%
Other values (18474)59781
99.0%
ValueCountFrequency (%)
110008
< 0.1%
1100111
< 0.1%
110024
 
< 0.1%
110039
< 0.1%
110046
< 0.1%
110056
< 0.1%
110065
< 0.1%
110078
< 0.1%
110087
< 0.1%
110095
< 0.1%
ValueCountFrequency (%)
294831
 
< 0.1%
294821
 
< 0.1%
294811
 
< 0.1%
294805
< 0.1%
294791
 
< 0.1%
294783
< 0.1%
294773
< 0.1%
294761
 
< 0.1%
294751
 
< 0.1%
294741
 
< 0.1%

sls_order_dt
Real number (ℝ)

High correlation  Skewed 

Distinct1127
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20123403
Minimum0
Maximum20140128
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2025-11-05T13:44:05.798567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20120409
Q120130402
median20130714
Q320131018
95-th percentile20131224
Maximum20140128
Range20140128
Interquartile range (IQR)616

Descriptive statistics

Standard deviation356972.28
Coefficient of variation (CV)0.017739161
Kurtosis3172.9843
Mean20123403
Median Absolute Deviation (MAD)308
Skewness-56.341085
Sum1.2154133 × 1012
Variance1.2742921 × 1011
MonotonicityNot monotonic
2025-11-05T13:44:05.895897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20131212263
 
0.4%
20131001246
 
0.4%
20130605241
 
0.4%
20131209238
 
0.4%
20130819237
 
0.4%
20131205227
 
0.4%
20131215222
 
0.4%
20131226220
 
0.4%
20131029220
 
0.4%
20131219219
 
0.4%
Other values (1117)58065
96.1%
ValueCountFrequency (%)
017
< 0.1%
54891
 
< 0.1%
321541
 
< 0.1%
201012295
 
< 0.1%
201012304
 
< 0.1%
201012315
 
< 0.1%
201101012
 
< 0.1%
201101025
 
< 0.1%
201101034
 
< 0.1%
201101043
 
< 0.1%
ValueCountFrequency (%)
2014012896
0.2%
2014012761
0.1%
2014012668
0.1%
2014012582
0.1%
2014012465
0.1%
2014012375
0.1%
2014012257
0.1%
2014012182
0.1%
2014012069
0.1%
2014011974
0.1%

sls_ship_dt
Real number (ℝ)

High correlation 

Distinct1124
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20129938
Minimum20110105
Maximum20140204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2025-11-05T13:44:05.993570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20110105
5-th percentile20120418
Q120130410
median20130721
Q320131025
95-th percentile20131231
Maximum20140204
Range30099
Interquartile range (IQR)615

Descriptive statistics

Standard deviation4851.0607
Coefficient of variation (CV)0.00024098736
Kurtosis7.5490007
Mean20129938
Median Absolute Deviation (MAD)308
Skewness-2.1619201
Sum1.215808 × 1012
Variance23532790
MonotonicityIncreasing
2025-11-05T13:44:06.087264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20131219263
 
0.4%
20131008246
 
0.4%
20130612241
 
0.4%
20131216238
 
0.4%
20130826237
 
0.4%
20131212227
 
0.4%
20131222222
 
0.4%
20131105220
 
0.4%
20140102220
 
0.4%
20131226219
 
0.4%
Other values (1114)58065
96.1%
ValueCountFrequency (%)
201101055
< 0.1%
201101064
< 0.1%
201101075
< 0.1%
201101082
 
< 0.1%
201101095
< 0.1%
201101104
< 0.1%
201101113
< 0.1%
201101123
< 0.1%
201101136
< 0.1%
201101143
< 0.1%
ValueCountFrequency (%)
2014020496
0.2%
2014020361
0.1%
2014020268
0.1%
2014020182
0.1%
2014013165
0.1%
2014013075
0.1%
2014012957
0.1%
2014012882
0.1%
2014012769
0.1%
2014012674
0.1%

sls_due_dt
Real number (ℝ)

High correlation 

Distinct1124
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20130104
Minimum20110110
Maximum20140209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2025-11-05T13:44:06.175187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20110110
5-th percentile20120423
Q120130415
median20130726
Q320131030
95-th percentile20140105
Maximum20140209
Range30099
Interquartile range (IQR)615

Descriptive statistics

Standard deviation4985.4532
Coefficient of variation (CV)0.00024766157
Kurtosis6.8220444
Mean20130104
Median Absolute Deviation (MAD)310
Skewness-1.9180773
Sum1.215818 × 1012
Variance24854744
MonotonicityIncreasing
2025-11-05T13:44:06.267840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20131224263
 
0.4%
20131013246
 
0.4%
20130617241
 
0.4%
20131221238
 
0.4%
20130831237
 
0.4%
20131217227
 
0.4%
20131227222
 
0.4%
20131110220
 
0.4%
20140107220
 
0.4%
20131231219
 
0.4%
Other values (1114)58065
96.1%
ValueCountFrequency (%)
201101105
< 0.1%
201101114
< 0.1%
201101125
< 0.1%
201101132
 
< 0.1%
201101145
< 0.1%
201101154
< 0.1%
201101163
< 0.1%
201101173
< 0.1%
201101186
< 0.1%
201101193
< 0.1%
ValueCountFrequency (%)
2014020996
0.2%
2014020861
0.1%
2014020768
0.1%
2014020682
0.1%
2014020565
0.1%
2014020475
0.1%
2014020357
0.1%
2014020282
0.1%
2014020169
0.1%
2014013174
0.1%

sls_sales
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean486.09872
Minimum-54
Maximum3578
Zeros2
Zeros (%)< 0.1%
Negative3
Negative (%)< 0.1%
Memory size472.0 KiB
2025-11-05T13:44:06.362149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-54
5-th percentile2
Q18
median30
Q3540
95-th percentile2443
Maximum3578
Range3632
Interquartile range (IQR)532

Descriptive statistics

Standard deviation928.50087
Coefficient of variation (CV)1.9101076
Kurtosis2.5126588
Mean486.09872
Median Absolute Deviation (MAD)25
Skewness1.9273089
Sum29355502
Variance862113.86
MonotonicityNot monotonic
2025-11-05T13:44:06.442380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
58825
 
14.6%
357832
 
13.0%
94467
 
7.4%
23190
 
5.3%
42375
 
3.9%
222120
 
3.5%
102023
 
3.3%
251789
 
3.0%
501735
 
2.9%
5401695
 
2.8%
Other values (38)24339
40.3%
ValueCountFrequency (%)
-541
 
< 0.1%
-351
 
< 0.1%
-181
 
< 0.1%
02
 
< 0.1%
23190
 
5.3%
42375
 
3.9%
58825
14.6%
8907
 
1.5%
94467
7.4%
102023
 
3.3%
ValueCountFrequency (%)
35781551
2.6%
3400185
 
0.3%
3375211
 
0.3%
24431145
1.9%
23841255
2.1%
23201215
2.0%
22951262
2.1%
2182758
1.3%
2071521
 
0.9%
2049554
 
0.9%

sls_quantity
Real number (ℝ)

Skewed 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0004139
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2025-11-05T13:44:06.506330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.044011487
Coefficient of variation (CV)0.043993277
Kurtosis30535.36
Mean1.0004139
Median Absolute Deviation (MAD)0
Skewness162.14808
Sum60423
Variance0.001937011
MonotonicityNot monotonic
2025-11-05T13:44:06.554548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
160387
> 99.9%
27
 
< 0.1%
51
 
< 0.1%
101
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%
ValueCountFrequency (%)
160387
> 99.9%
27
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
51
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%
27
 
< 0.1%
160387
> 99.9%

sls_price
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean486.00813
Minimum-1701
Maximum3578
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)< 0.1%
Memory size472.0 KiB
2025-11-05T13:44:06.631113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1701
5-th percentile2
Q18
median30
Q3540
95-th percentile2443
Maximum3578
Range5279
Interquartile range (IQR)532

Descriptive statistics

Standard deviation928.53838
Coefficient of variation (CV)1.9105408
Kurtosis2.5129464
Mean486.00813
Median Absolute Deviation (MAD)25
Skewness1.9270386
Sum29350517
Variance862183.52
MonotonicityNot monotonic
2025-11-05T13:44:06.710497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
58825
 
14.6%
357834
 
13.0%
94469
 
7.4%
23191
 
5.3%
42376
 
3.9%
222120
 
3.5%
102024
 
3.4%
251788
 
3.0%
501736
 
2.9%
5401695
 
2.8%
Other values (36)24333
40.3%
ValueCountFrequency (%)
-17011
 
< 0.1%
-7691
 
< 0.1%
-301
 
< 0.1%
-221
 
< 0.1%
-211
 
< 0.1%
23191
 
5.3%
42376
 
3.9%
58825
14.6%
8907
 
1.5%
94469
7.4%
ValueCountFrequency (%)
35781551
2.6%
3400185
 
0.3%
3375211
 
0.3%
24431145
1.9%
23841255
2.1%
23201215
2.0%
22951262
2.1%
2182758
1.3%
2071521
 
0.9%
2049554
 
0.9%

Interactions

2025-11-05T13:44:03.778653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:00.462362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.043660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.601848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.156066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.692588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.227336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.851579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:00.548326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.129163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.690604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.237346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.781390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.315618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.935909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:00.634636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.213358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.776654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.317739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.864600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.378833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:04.012629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:00.722903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.299408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.853879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.397284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.934329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.472790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:04.078471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:00.809206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.379638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.920227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.475394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.022847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.560988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:04.163517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:00.890343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.460959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.002784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.556340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.084615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.626584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:04.229106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:00.967641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:01.526479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.069143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:02.628791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.160133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T13:44:03.704673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-05T13:44:06.770406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
sls_cust_idsls_due_dtsls_order_dtsls_pricesls_quantitysls_salessls_ship_dt
sls_cust_id1.0000.0540.054-0.025-0.003-0.0260.054
sls_due_dt0.0541.0000.999-0.219-0.006-0.2201.000
sls_order_dt0.0540.9991.000-0.219-0.006-0.2190.999
sls_price-0.025-0.219-0.2191.0000.0011.000-0.219
sls_quantity-0.003-0.006-0.0060.0011.0000.002-0.006
sls_sales-0.026-0.220-0.2191.0000.0021.000-0.220
sls_ship_dt0.0541.0000.999-0.219-0.006-0.2201.000

Missing values

2025-11-05T13:44:04.477690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-05T13:44:04.577758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-05T13:44:04.693666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sls_ord_numsls_prd_keysls_cust_idsls_order_dtsls_ship_dtsls_due_dtsls_salessls_quantitysls_price
0SO43697BK-R93R-62217682010122920110105201101103578.013578.0
1SO43698BK-M82S-44283892010122920110105201101103400.013400.0
2SO43699BK-M82S-44258632010122920110105201101103400.013400.0
3SO43700BK-R50B-6214501201012292011010520110110699.01699.0
4SO43701BK-M82S-44110032010122920110105201101103400.013400.0
5SO43702BK-R93R-44276452010123020110106201101113578.013578.0
6SO43703BK-R93R-62166242010123020110106201101113578.013578.0
7SO43704BK-M82B-48110052010123020110106201101113375.013375.0
8SO43705BK-M82S-38110112010123020110106201101113400.013400.0
9SO43706BK-R93R-48276212010123120110107201101123578.013578.0
sls_ord_numsls_prd_keysls_cust_idsls_order_dtsls_ship_dtsls_due_dtsls_salessls_quantitysls_price
60388SO75120SJ-0194-X1874920140128201402042014020954.0154.0
60389SO75120CA-1098187492014012820140204201402099.019.0
60390SO75121TT-M928152512014012820140204201402095.015.0
60391SO75121TI-M8231525120140128201402042014020935.0135.0
60392SO75121HL-U509-R1525120140128201402042014020935.0135.0
60393SO75122FE-66541586820140128201402042014020922.0122.0
60394SO75122CA-1098158682014012820140204201402099.019.0
60395SO75123FE-66541875920140128201402042014020922.0122.0
60396SO75123ST-140118759201401282014020420140209159.01159.0
60397SO75123CA-1098187592014012820140204201402099.019.0